A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
• Ξϊςʔγϣϯ͕༩͞Ε͍ͯΔը૾ΛूΊͯɺ εϥΠε ʢ CPPLNBSLTʣͷσʔληοτΛߏங • #PPLNBSLใΛCPVOEJOHCPYʹมͯ͠ମݕग़ͷΞϊςʔ γϣϯ͖σʔληοτͱͯ͠ެ։ Yan K, Wang X, Lu L, Summers RM. DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imag. 2018;5(3):36501. RECIST .. response evaluation criteria in solid tumors
நग़Λߦ͍ɺ֤ΤϯςΟςΟ͕తͷCPPLNBSLʹඥͮ ͔͘ʹ͍ͭͯ$//ʴ4FMGBUUFOUJPOͰྨͨ͠ • ࠷ߴਫ਼ͱͯ͠'TDPSFΛهͨ͠ ը૾தͷͷۣܗ͕Ϩϙʔτͷ#00,."3,ʹඥͮ͘පม Peng Y, Yan K, Sandfort V, Summers RM, Lu Z. A self-attention based deep learning method for lesion attribute detection from CT reports. arXiv. 2019;1904.13018.
NBTT BDVUFTUSPLF 'B[FLBT • #JP#&35ʢ-FFFUBMʣ "UUFOUJPOͷྨثΛ࡞ͬͯɺ̎ͭͷྨΛղ͍ͨ • $PBSTF (SBOVMBS$MBTTJGJDBUJPOͷͦΕͧΕͰBDDVSBDZɾΛه • (SBOVMBS$MBTTJGJDBUJPOͰFYQFSJFODFEOFVSPSBEJPMPHJTUʹۇ͔ʹྼΔͷͷɺFYQFSJFODFE OFVSPMPHJTU TUSPLFQIZTJDJBOΛ্ճΔྨੑೳΛهͨ͠ͱ͍ͯ͠Δ David A. Wood, Jeremy Lynch, Sina Kafiabadi, et al. 2020. Automated labelling using an attention model for radiology reports of MRI scans (ALARM). volume 121 of Proceedings of Machine Learning Research, pages 811– 826, Montreal, QC, Canada. PMLR. Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–40.
ϞσϧWBOJMMB#&35ʢϚϧνϥϕϧͷྨثΛग़ྗͱ͠ɺ ֤ϥϕϧΛʮ1PTJUJWF /FHBUJWF 6ODFSUBJO #MBOLʯͰྨʣ Λ༻ҙ Smit A, Jain S, Rajpurkar P, Pareek A, Ng AY, Lungren MP. CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT. arXiv. 2020;2004.09167. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv. 2019;1901.0703.
ʯͳͲΛൺֱ • ·ͨɺ༁λεΫͰ༻͍ΒΕΔ#BDLUSBOTMBUJPOΛ༻͍ͯ%BUBBVHNFOUBUJPOͨ݁͠Ռͱൺֱ • ݁Ռʮ#MVF#&35 #BDLUSBOTMBUJPO"VHNFOUBUJPOʯ͕࠷ߴਫ਼ʢ'TDPSFʣ • ॴݟͷ༗ແΛͷॴݟϥϕϧʹݶఆ͓ͯ͠ΓɺͦͷଞͷॴݟʹదԠ͕ग़དྷͳ͍՝ͱ͍ͯ͠Δ Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–40. Huang K, Altosaar J. ClinicalBert: Modeling Clinical Notes and Predicting Hospital Readmission. arXiv. 2019;1904.05342. Peng Y, Yan S, Lu Z. Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. In: Proceedings ofthe BioNLP 2019 workshop. Association for Computational Linguistics; 2019. p. 58–65. Smit A, Jain S, Rajpurkar P, Pareek A, Ng AY, Lungren MP. CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT. arXiv. 2020;2004.09167.
• 3&ʢ.*.*$$93$IF9QFSUʣ • σʔληοτʹ͓͚ΔΞϊςʔλʔؒͷෆҰகʢΤϯςΟ ςΟͷཻͳͲʣͷ՝ʹ͍ͭͯߟ͍ͯ͠Δ Jain S, Agrawal A, Saporta A, Truong SQ, Duong DN, Bui T, et al. RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. arXiv. 2021;2016.14463. Wu S, He Y. Enriching Pre-trained Language Model with Entity Information for Relation Classification. arXiv. 2019;1905.08284. R-BERT
Jain S, Smit A, Qh S, Vinbrain T, Chanh V, Nguyen DT, et al. VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels; VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels. In: Proceedings of the Conference on Health, Inference, and Learning. 2021. p. 105–115. Luke Oakden-Rayner. 2019. Exploring large scale public medical image datasets. arXiv:1907.12720
• ը૾͔Β༩ͨ͠ϥϕϧΛ(SPVOE5SVUIͱͯ͠ɺϨϙʔτ͔Β༩ͨ͠ॴݟϥϕϧͱͷҰகΛܭࢉ ͨ͠ͱ͜ΖɺฏۉͰ'είΞ͕ͱ͍݁ՌͰ͋ͬͨ Jain S, Smit A, Qh S, Vinbrain T, Chanh V, Nguyen DT, et al. VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels; VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels. In: Proceedings of the Conference on Health, Inference, and Learning. 2021. p. 105–115. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv. 2019;1901.0703.
ͦͷ࣌ͷใ͔͠ࢀরͰ͖ͳ͍ɺϨϙʔτ͕ʮ*NQSFTTJPO ηΫγϣϯʯ͔ΒͷΈϥϕϦϯά͢ΔͷͰใ͕མ͍ͪͯΔՄ ೳੑ͕͋ΔͳͲ͕ݪҼͱͳ͍ͬͯΔՄೳੑ͕ࢦఠ͞Εͨ Jain S, Smit A, Qh S, Vinbrain T, Chanh V, Nguyen DT, et al. VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels; VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels. In: Proceedings of the Conference on Health, Inference, and Learning. 2021. p. 105–115. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv. 2019;1901.0703.
ͯ͠༗ҙͳվળ͕֬ೝ͞Εͨ Jain S, Smit A, Qh S, Vinbrain T, Chanh V, Nguyen DT, et al. VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels; VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels. In: Proceedings of the Conference on Health, Inference, and Learning. 2021. p. 105–115.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
Λ༻ҙͨ͠ • ഏ؞ͷ༗ແʹ͍ͭͯ"6$͕ɺѱԽͷྨɺվળͷ ྨͷਫ਼Λୡͨ͠ Kehl KL, Elmarakeby H, Nishino M, Van Allen EM, Lepisto EM, Hassett MJ, et al. Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports. JAMA Oncol. 2019;5(10):1421–9.
• ܦ࣌తมԽʢѱԽʣΛΠϕϯτͱͯ͠ɺͦΕ͕ظؒʹൃ ੜ͢Δ͔ɺଧͪΓʹͳΔ͔Λੳͨ͠ • ਓखͰऩूͯ͠࡞ͨ͠ੜଘۂઢͱϞσϧ͔Βಘͨ݁Ռ͕ ͍ۙ݁ՌʹͳΔ͜ͱΛ֬ೝͨ͠ Kehl KL, Elmarakeby H, Nishino M, Van Allen EM, Lepisto EM, Hassett MJ, et al. Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports. JAMA Oncol. 2019;5(10):1421–9.
Bozkurt S, Alkim E, Banerjee I, Rubin DL. Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm. J Digit Imaging. 2019;32:544–53.
ମͷͩͬͨ Bozkurt S, Alkim E, Banerjee I, Rubin DL. Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm. J Digit Imaging. 2019;32:544–53.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
• ͔͠͠ɺϨϙʔτϑϦʔςΩετͰߏԽ͞Ε͍ͯͳ͍ͷͰɺ͔ͦ͜ΒࢹͰ༰Λ֬ೝͯ͠ΨΠυ ϥΠϯʹద༻ͤ͞Δͷඇৗʹ͕͔͔࣌ؒΔ • ຊݚڀͰɺϑΥϩʔΞοϓ͕ඞཁͳ*-/Λಛఆ͢ΔΞϧΰϦζϜΛ։ൃ͠ɺͦͷޙɺͦͷϨϙʔτ͕ 'MFJTDIOFS 4PDJFUZHVJEFMJOFʹԊͬͨϑΥϩʔΞοϓ͕ߦΘΕ͔ͨΛධՁͨ͠ S. K. Kang, K. Garry, R. Chung, W. H. Moore, E. Iturrate, J. L. Swartz, D. C. Kim, L. I. Horwitz, and S. Blecker, ‘‘Natural language processing for identification of incidental pulmonary nodules in radiology reports,’’ J. Amer. College Radiol., vol. 16, no. 11, pp. 1587–1594, Nov. 2019 MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228–43.
݁અ͕ʮมԽͳ͠ɺ҆ఆ͍ͯ͠ΔʯͳͲͱॻ͔Ε͍ͯΔ߹JODJEFOUBMͷج४͔Βআ֎ɻಉ༷ʹྑੑ Ͱ͋Δ͜ͱ͕֬ఆతͳ݁અʢੴփԽͷྑੑύλʔϯʣͳͲJODJEFOUBMͷج४͔Βআ֎ɻ • ͜ΕΒͷϧʔϧʹԊͬͯ์ࣹઢՊҩ͕Ϩϙʔτͷ*-/ʹΞϊςʔγϣϯΛߦͬͨ ʢຕͷϨϙʔτͷ͏ͪɺຕͷϨϙʔτʹ*-/ͷΞϊςʔγϣϯ͕༩͞Εͨʣ S. K. Kang, K. Garry, R. Chung, W. H. Moore, E. Iturrate, J. L. Swartz, D. C. Kim, L. I. Horwitz, and S. Blecker, ‘‘Natural language processing for identification of incidental pulmonary nodules in radiology reports,’’ J. Amer. College Radiol., vol. 16, no. 11, pp. 1587–1594, Nov. 2019 Swartz J, Koziatek C, Theobald J, Smith S, Iturrate E. Creation of a simple natural language processing tool to support an imaging utilization quality dashboard. Int J Med Inform. 2017;101:93–9.
• ͦͷ͏ͪɺͷϨίϝϯσʔγϣϯͷ༰͕ΨΠυϥΠϯͱҰக͍ͯͨ͠ S. K. Kang, K. Garry, R. Chung, W. H. Moore, E. Iturrate, J. L. Swartz, D. C. Kim, L. I. Horwitz, and S. Blecker, ‘‘Natural language processing for identification of incidental pulmonary nodules in radiology reports,’’ J. Amer. College Radiol., vol. 16, no. 11, pp. 1587–1594, Nov. 2019 Swartz J, Koziatek C, Theobald J, Smith S, Iturrate E. Creation of a simple natural language processing tool to support an imaging utilization quality dashboard. Int J Med Inform. 2017;101:93–9.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
Ϟσϧ(36Λ༻ɻೖྗͷຒΊࠐΈදݱͱͯ͠ɺ୯ޠʹՃ͑ͯ ʮจࣈɾ෦टʯͳͲͷใΛར༻ • நग़ͨ͠ΤϯςΟςΟͷඥ͚ʢάϧʔϓԽʣΤϯςΟςΟͷ ҐஔใΛ༻͍ͯϧʔϧϕʔεͰॲཧ • ΤϯςΟςΟநग़ͷੑೳʢ'4DPSFʣͱߴ͔͕ͬͨɺ άϧʔϓԽͷੑೳʢ"DDVSBDZʣͱϧʔϧϕʔεͷख๏ʹ վળͷ༨͕͋ͬͨͱ͍ͯ͠Δ Xie Z, Yang Y, Wang M, Li M, Huang H, Zheng D, et al. Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports. Methods Inf Med. 2019;58:94–106.
• σʔληοτͱͯ͠ɺ$5ϨϙʔτʹΞϊςʔγϣ ϯΛߦ͍ɺຕͷ؊͕Μʹؔ͢ΔϨϙʔτΛूΊ ͨɻͦͷଞɺ؊ߗมɾ؊೯๔ɾ݂जͳͲͷྫʹ ؔ͢ΔϨϙʔτΛຕूΊͨɻ Liu H, Xu Y, Zhang Z, Wang N, Huang Y, Hu Y, et al. A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis. IEEE 2020 Aug 28;8:159110-159119.
ΤϯςΟςΟͷछྨʮ-PDBUJPOɾ.PSQIPMPHZɾ%FOTJUZɾ&OIBODFNFOUɾ.PEJGJFSʯͷ̑ͭ • நग़ͨ͠ΤϯςΟςΟͷ༻ޠɺࣄલʹ༻ҙͨ͠ಉٛޠϦετʹج͖ͮɺਖ਼نԽ͞Εͨ • ΤϯςΟςΟநग़ͷੑೳɺશମͷ'4DPSFͰΛୡ Liu H, Xu Y, Zhang Z, Wang N, Huang Y, Hu Y, et al. A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis. IEEE 2020 Aug 28;8:159110-159119.
• ෳͷྨثͰ࣮ݧΛߦ͍ɺ3BOEPN'PSFTUͰ࠷ߴੑೳ Λهʢ'4DPSFʀʣ • Τϥʔੳͷ݁Ռɺ؊͕ΜͱࣅͨಛΛ࣋ͭ؊ߗมʹؔ͢ ΔϨϙʔτِ͕ཅੑͱͯ͠ଟ͘ݕग़͞Εͨͱ͍ͯ͠Δ Liu H, Xu Y, Zhang Z, Wang N, Huang Y, Hu Y, et al. A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis. IEEE 2020 Aug 28;8:159110-159119.
FUBMʣʯΛ༻͍ͨྨϞσϧΛ༻ҙ • ίʔσΟϯάࡁΈͷϨϙʔτ͔ΒຕΛαϯϓϦϯά͠ɺੑೳධՁΛߦͬͨ • ධՁηοτͱͯ͠ɺఆٛͨ͠ϧʔϧʹج͖ͮɺෳͷ์ࣹઢՊҩͰ֤ϨϙʔτʹόΠφϦΛ༩ Wadia R, Akgun K, Brandt C, Fenton BT, Levin W, Marple AH, Garla V, Rose MG, Taddei T TC. Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer. JCO Clin Cancer Inf. 2018;2:1–7. Savova GK, Masanz JJ, Ogren PV, et al: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications. J Am Med Inform Assoc 17:507-513, 2010
ͷ߹ɺ ʮ͕ΜҎ֎ͷಡӨґཔʯͩͱײ͕ େ͖͘Լ͍ͯͨ͠ʣ Wadia R, Akgun K, Brandt C, Fenton BT, Levin W, Marple AH, Garla V, Rose MG, Taddei T TC. Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer. JCO Clin Cancer Inf. 2018;2:1–7.
2VBMJUZBOEDPNQMJBODF ü Ϩϙʔτͷ༰͔Βྍͷ࣭҆શੑΛධՁ͢Δ͜ͱΛࢦ͢ $PIPSUBOEFQJEFNJPMPHZ ü Ϩϙʔτ͔ΒྟচݚڀͷͨΊͷίϗʔτΛ࡞͢Δ͜ͱΛࢦ͢ -BOHVBHFEJTDPWFSZLOPXMFEHF ü ϨϙʔτΛղੳ͠ɺஅࢧԉԁͳίϛϡχέʔγϣϯͷͨΊʹͲ͏࠷దԽ͢Δ͔ʹ͍ͭͯௐΔ 5FDIOJDBM/-1 ü Ϩϙʔτʹ͓͚Δ൱ఆදݱͷݕग़εϖϧޡΓमਖ਼ͷΑ͏ͳࣗવݴޠॲཧͷࠜװతͳٕज़ʹؔ͢Δͷ Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inf Decis Mak. 2020;21:179.
Α͏ͳʮ$FSUBJOUZUFSNTʯͱஅͷΒ͠͞Λࣔ͢ج४Λ࡞ͬͨ • ͜ΕΒͷϧʔϧΛࣗӃͰద༻͠ɺ์ࣹઢՊҩʹಡӨ࣌ʹ͜ͷج४ Λࢀߟͱ͢ΔΑ͏ʹଅͨ͠ • ͜ͷج४͕࠷దͱݶΒͳ͍͕ɺґཔҩͱಡӨҩͷؒͰڞ௨ͷϧʔ ϧΛڞ༗͢Δ͜ͱͰίϛϡχέʔγϣϯ͕վળͰ͖Δͱ͍ͯ͠Δ Panicek DM, Hricak H. How sure are you, doctor? A standardized lexicon to describe the radiologist’s level of certainty. AJR Am J Roentgenol 2016;207:2–3 Khorasani R, Bates DW, Teeger S, Rothschild JM, Adams DF, Seltzer SE. Is Terminology Used Effectively to Convey Diagnostic Certainty in Radiology Reports? Acad Radiol. 2003;10:685–8.
͞Ε͍ͯͨ • ʮෆ࣮֬ੑʯΛࣔ͢දݱͷසͱܦྺͱͷ૬ؔݟΒΕͳ͔ͬͨ Callen AL, Dupont SM, Price A, Laguna B, Mccoy D, Do B, et al. Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing. J Digit Imaging. 2020;33(5):1194–201. Rosenkrantz AB, Kiritsy M, Kim S. How “consistent” is “consis- tent”? A clinician-based assessment ofthe reliability ofexpressions used by radiologists to communicate diagnostic confidence. Clin Radiol. 2014;69(7):745–9
• #JP#&35 -FFFUBM ΛGJOFUVOJOHͨ݁͠Ռ͕࠷ߴείΞʢ"6$ʣΛهͨ͠ Liu F, Zhou P, Baccei SJ, Masciocchi MJ, Amornsiripanitch N, Kiefe CI, et al. Qualifying Certainty in Radiology Reports through Deep Learning-Based Natural Language Processing. Am J Neuroradiol. 2021;42(10):1755–61. Panicek DM, Hricak H. How sure are you, doctor? A standardized lexicon to describe the radiologist’s level of certainty. AJR Am J Roentgenol 2016;207:2–3 Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–40.
ؔ৺ͷ͋ΔରΛࣔ͢ʮ53"+&$503ʯ ͦͷॴΛࣔ͢ʮ-"/%."3,ʯͱ ͱͷؔΛࣔ͢લஔࢺͷʮ41"5*"-*/%*$"503ʯ • Ϩϙʔτͷʮ41"5*"-*/%*$"503ʯΛೝࣝ͠ɺͦΕʹඥͮ͘4QBUJBMSPMFʢ53"+&$503 -"/%."3,)&%(&%*"(/04*4ʣΛநग़͠ɺߏԽ͢Δ͜ͱΛࢼΈͨ P. Kordjamshidi, M.V. Otterlo, M.-F. Moens, Spatial Role Labeling: Task Definition and Annotation Scheme, in: Proceedings of the Language Resources & Evaluation Conference, 2010, pp. 413–420. Datta S, Si Y, Rodriguez L, Shooshan SE, Demner-Fushman D, Roberts K. Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning. J Biomed Inform. 2020;32.
ݸผͷϥϕϧͩͱʮ)&%(&%*"(/04*4ʯֶशαϯϓϧ͕গͳ͔ͬͨ͜ͱɺ 41"5*"- */%*$"503ͱͷڑ͕େ͖͔ͬͨ͜ͱ͕ݪҼͰείΞ͕͔ͬͨͱߟ͍ͯ͠Δ Datta S, Si Y, Rodriguez L, Shooshan SE, Demner-Fushman D, Roberts K. Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning. J Biomed Inform. 2020;32.
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